A complexity approach is reduction-based if it investigates a model complexity problem by reducing deep networks to some known problems and functions, and does not define any explicit complexity measure. For example, Arora et al. [1]} build connections between deep networks with ReLU activation functions and Lebesgue spaces. Khrulkov et al. [2]} connect deep neural networks with several types of tensor decompositions.
